淡江大學機構典藏:Item 987654321/120863
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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/120863


    Title: Parameter estimation for the composite dynamic systems based on sequential order statistics from Burr type XII mixture distribution
    Authors: Tsai, Tzong-Ru;Lio, Yuhlong;Xin, Hua;Pham, Hoang
    Keywords: composite dynamical systems;hazard rate;Markov chain Monte Carlo;mixture distribution;sequential order statistics
    Date: 2021-04-08
    Issue Date: 2021-06-11 12:13:38 (UTC+8)
    Publisher: MDPI AG
    Abstract: Considering the impact of the heterogeneous conditions of the mixture baseline distribution on the parameter estimation of a composite dynamical system (CDS), we propose an approach to infer the model parameters and baseline survival function of CDS using the maximum likelihood estimation and Bayesian estimation methods. The power-trend hazard rate function and Burr type XII mixture distribution as the baseline distribution are used to characterize the changes of the residual lifetime distribution of surviving components. The Markov chain Monte Carlo approach via using a new Metropolis–Hastings within the Gibbs sampling algorithm is proposed to overcome the computation complexity when obtaining the Bayes estimates of model parameters. A numerical example is generated from the proposed CDS to analyze the proposed procedure. Monte Carlo simulations are conducted to investigate the performance of the proposed methods, and results show that the proposed Bayesian estimation method outperforms the maximum likelihood estimation method to obtain reliable estimates of the model parameters and baseline survival function in terms of the bias and mean square error
    Relation: Mathematics 9(8), 810
    DOI: 10.3390/math9080810
    Appears in Collections:[Graduate Institute & Department of Statistics] Journal Article

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